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@KrishnaPG

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Big data Consultant (Predictive Analytics, M2M Telematics)

$125 USD / Jam

Bendera

India (1:36 PTG)

Menyertai pada Mei 29, 2005

$125 USD / Jam

Gopalakrishna, widely referred to as GK, is a Technology Management & Strategy Consultant specialized in Big data, Cloud and Distributed Predictive Analytics. (Web: [login to view URL]) Recent Big-data Predictive Analytics solutions by Gopalakrishna: • Fleet management with Predictive Maintenance • Fire Spread Models for Public Safety • Epidemic Outbreak Detection for Healthcare • Sentiment Analysis for social media • Text-mining health-care documents for HIE For more than a decade GK is being actively engaged by companies like Microsoft, Oracle and Symphony on various tasks ranging from applying Machine Learning to predict component failure and machine reliability, to designing RESTful Single-Sign-On (SSO) architectures for Oracle IDM Fusion Middleware, distributed CRM systems on Windows Azure for high-availability, low-cost Render Farm solutions on Hadoop for high-scalability and so on. Gopalakrishna has proven track record in various research fields ranging from Computational Automata Theory, Artificial Intelligence, Advanced Analytics, to Systems Control and Simulation & Modeling. His contributions to Open-source community created multiple innovative solutions such as CarMusTy, CFugue, PhTranslator, .Net Obfuscator so on. Not to mention, Gopalakrishna's business strategy helped many customers build their product portflios strong and setup Center-of-Excellences. More about his work can be found at: [login to view URL]

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Establishing the existence of uncountable number of Accelerated Turing Machines

MSDN

We examine the converse of Church-Turing thesis and establish the existence of uncountable number of Accelerated Turing machines, which leads to the conclusion that these machines are unaffected by Gödel's incompleteness theorem.

On Solving the System

Proc. of the International Conference on Systemic, Cybernetics and Informatics

Mathematically a system is said to be solved if its future states can be predicted from the information provided by the present and past state history. In this paper we present a way of solving artificial life systems using the principles of state-machines. We present the view of manipulating the artificial systems considering them as being embedded in external program entities. Further, we discuss the technique of using algorithmic transformations to understand the behavioral complexity of virtual organisms.

Data Dependencies and Learning in Artificial Systems

Proc. of Approaches and Applications of Inductive Programming

Data-dependencies play an important role in the performance of learning algorithms. In this paper we analyze the concepts of data dependencies in the context of artificial systems. When a problem and its solution are viewed as points in a system configuration, variations in the problem configurations can be used to study the variations in the solution configurations and vice versa. These variations could be used to infer solutions to unknown instances of problems based on the solutions to known instances, thus

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